Top AI & Machine Learning Jobs

Own the full lifecycle of AI products, from discovery and roadmap definition through prototyping, deployment, adoption, and measurable business outcomes. Build GenAI and agentic AI solutions using LLM APIs, tool calling, structured outputs, and evaluation. Partner with data engineering on pipelines, data quality, governance, security, and observability. Lead agile backlog management, UX collaboration, training, change management, and continuous improvement while translating ambiguous business needs into production AI applications.

Five-month internship focused on applying data analytics and machine learning to semiconductor manufacturing data. The intern will query and preprocess large-scale quality datasets, develop models for quality-metric drift detection, perform multivariable analysis and optimization, create visualizations, and automate engineering workflows. Responsibilities include collaborating with engineering teams, documenting results, and presenting recommendations. Relevant backgrounds include engineering, statistics, mathematics, computer science, data science, or another analytics-related discipline.

Develop and deploy machine learning, statistical modeling, and predictive analytics solutions for semiconductor manufacturing and business operations. Analyze large datasets, build scalable data workflows, conduct exploratory and root-cause analysis, and partner with engineering and software teams to operationalize models. Lead analytics projects, communicate insights to technical and business stakeholders, mentor junior data scientists, and ensure responsible, secure, compliant use of AI and data.

Build and productionize machine learning models for semiconductor quality and manufacturing processes. Develop agentic AI, LLM, retrieval-augmented generation, tool-use, and memory capabilities for predictive quality workflows. Create maintainable analytical pipelines, optimize performance and token efficiency, and support testing, deployment, and monitoring. Communicate analytical findings to technical and non-technical stakeholders while applying AI tools responsibly.

Design, develop, and maintain scalable full-stack applications across frontend, API, and backend layers. Build AI-native systems using LLMs, retrieval-augmented generation, agentic workflows, and intelligent automation. Translate business requirements into secure, reliable solutions; manage the full software lifecycle; resolve performance and stability issues; and collaborate cross-functionally to deliver enterprise-scale applications.

Architect and deploy autonomous Agentic AI systems for smart manufacturing. Responsibilities include developing multi-step AI agents, integrating LLMs with APIs and databases, building evaluation and profiling frameworks, implementing MCP and A2A interoperability, deploying agents through CI/CD to Kubernetes and cloud containers, securing agent workflows, optimizing inference performance, and mentoring engineers.

Lead the architecture and delivery of production AI, machine learning, data science, and agentic AI systems for semiconductor engineering. Design end-to-end pipelines, develop and evaluate models, deploy inference services, establish monitoring and guardrails, and guide technical roadmaps. Apply prediction, diagnosis, optimization, retrieval, and workflow automation to engineering challenges while mentoring engineers and communicating progress, risks, and model limitations to technical and executive stakeholders.

Supports the AI Process Intelligence team through Python automation prototypes, AI agent proofs of concept, predictive modeling, data pipelines, system connectors, controlled model testing, and technical documentation. Works under senior engineers and process architects in a structured, non-production environment while learning AI architecture, automation workflows, API integration, Agile development, DevOps, and knowledge integration.

Develop AI-enabled automation workflows for NAND device characterization and engineering data analysis. Build data pipelines, machine learning models, anomaly detection tools, dashboards, AI assistants, and automated reporting solutions using large-scale semiconductor datasets. The role involves applying data science, generative AI, LLMs, and agentic AI to improve engineering productivity and decision-making while collaborating with cross-functional engineering teams.

Designs, develops, deploys, and supports end-to-end AI applications for smart manufacturing. Responsibilities include building data pipelines, predictive models, APIs, web applications, LLM and agentic AI integrations, robotics and MES integrations, containerized services, and automated ML systems. The role also contributes to CI/CD, monitoring, model evaluation, Responsible AI, optimization, testing, documentation, and production support across global manufacturing operations.